Adobe Analytics Mastery: Workspace, Segmentation & Metrics

Data science and Marketing are two of the key driving forces that help companies create value and maintain an edge in today’s fast-paced economy.Welcome to…Customer Analytics in Python – the place where marketing and data science meet!This course offers a unique opportunity to acquire a rare and extremely valuable skill set.What will you learn in this course?This course is packed with knowledge, covering some of the most exciting methods used by companies, all implemented in Python.Customer Analytics is a broad field, so we've divided this course into five distinct parts, each highlighting different strengths and challenges within the analytical process.Here are the five major parts:1. We will introduce you to the relevant theory that you need to start performing customer analyticsWe have kept this part as short as possible in order to provide you with more practical experience. Nonetheless, this is the place where marketing beginners will learn about the marketing fundamentals and the reasons why we take advantage of certain models throughout the course.2. Then we will perform cluster analysis and dimensionality reduction to help you segment your customersBecause this course is based in Python, we will be working with several popular packages - NumPy, SciPy, and scikit-learn. In terms of clustering, we will show both hierarchical and flat clustering techniques, ultimately focusing on the K-means algorithm. Along the way, we will visualize the data appropriately to build your understanding of the methods even further. When it comes to dimensionality reduction, we will employ Principal Components Analysis (PCA) once more through the scikit-learn (sklearn) package. Finally, we’ll combine the two models to reach an even better insight about our customers. And, of course, we won’t

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